The present invention is in the field of systems, methods, and computer program products for data perturbation and anonymization using one-way hash.
Anonymizing data relies on removing or modifying the identifying variable(s) contained in the data, also known as Personally Identifiable Information (PII). Typically, an identifying variable is one that describes a characteristic of a person that is observable, that is registered (identification numbers, such as, social security number, employee ID, patient ID, etc.), or generally, that can be known to other persons. Anonymizing data keeps the referenced person's privacy as a priority while giving attention to a data miner's needs (e.g., an analyst examining the data for identification of trends, patterns, etc.).
Aggregating employee records to allow for data mining (e.g., identifying common patterns of top performers based on employee ratings) links all employee records across an organization. Moreover, data is often shared between organizations with data mining companies (e.g., surveyors, researchers, analysts, etc.). Anonymizing prevents the data miner from identifying the employees referenced in a data set.